I have the following dataframe:
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(3, 8), index=['A', 'B', 'C'], columns=index)
df.loc["B", (slice(None), 'two')]=np.nan
Now, I want to forward fill inplace the data for columns "baz" and "foo" (so not for columns "bar" and "qux"). I tried:
df[["baz", "foo"]].ffill(inplace=True)
but the resulting dataframe did not forward fill any of the values. How can I create a dataframe with forward filled data for those two columns only?
I believe the problem comes due to the inplace=True setting. Try accessing the slice with df.loc and then assigning the ffilled dataframe slice back:
df.loc[:, ["baz", "foo"]] = df[["baz", "foo"]].ffill()
Output:
first baz foo
second one two one two
A 0.465254 0.629161 -0.176656 -1.263927
B 2.051213 0.629161 1.539584 -1.263927
C -0.463592 -0.240445 -0.014090 0.170188
Alternatively, you could use df.fillna(method='ffill'):
df.loc[:, ["baz", "foo"]] = df[["baz", "foo"]].fillna(method='ffill')